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dc.contributor.authorPark, Solip
dc.date.accessioned2020-10-29T11:49:28Z
dc.date.available2020-10-29T11:49:28Z
dc.date.issued2020
dc.identifier.citationPark, S. Systematic discovery of germline cancer predisposition genes through large-scale cancer genomics. A: . Barcelona Supercomputing Center, 2020, p. 59-60.
dc.identifier.urihttp://hdl.handle.net/2117/330969
dc.description.abstractThe genetic causes of cancer include both somatic mutations and inherited germline variants. Large-scale tumor sequencing has revolutionized the identification of somatic driver alterations but has had limited impact on the identification of cancer predisposition genes (CPGs). Here we present a statistical method, ALFRED, that tests Knudson’s two-hit hypothesis to systematically identify CPGs from cancer genome data. Applied to ~10,000 tumor exomes the approach identifies known and putative CPGs – including the chromatin modifier NSD1 – that contribute to cancer through a combination of rare germline variants and somatic loss-of-heterozygosity (LOH). Rare germline variants in these genes contribute substantially to cancer risk, including to ~14% of ovarian carcinomas, ~7% of breast tumors, ~4% of uterine corpus endometrial carcinomas, and to a median of 2% of tumors across 17 cancer types. At the end of my scientific talk, I want to share my personal experiences as a female scientist in the field of computational biology.
dc.format.extent2 p.
dc.languageen
dc.language.isoeng
dc.publisherBarcelona Supercomputing Center
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.subjectÀrees temàtiques de la UPC::Informàtica::Arquitectura de computadors
dc.subject.lcshHigh performance computing
dc.titleSystematic discovery of germline cancer predisposition genes through large-scale cancer genomics
dc.typeConference report
dc.subject.lemacCàlcul intensiu (Informàtica)
dc.rights.accessOpen Access
local.citation.startingPage59
local.citation.endingPage60


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